Predicting Heart Disease Using Clinical Variables.zip
The Heart Disease Prediction dataset provides vital insight in the relationship between risk factors and cardiac health. This dataset contains 270 case studies of individuals classified as either having or not having heart disease based on results from cardiac catheterizations - the gold standard in heart health assessment. Each patient is identified by 13 independent predictive variables revealing their age, sex, chest pain type, blood pressure measurements, cholesterol levels, electrocardiogram results, exercise-induced angina symptoms, and the number of vessels seen on fluoroscopy showing narrowing of their coronary arteries. Provided with this data set is an opportunity to evaluate how these characteristics interact with each other in order to determine an individual’s level of risk for developing cardiovascular problems that lead to heart failure or stroke. With this knowledge we can create preventative strategies beyond what traditional medical treatment can do by identifying those at risk earlier and aid our healthcare professionals in treating them better. By analyzing a combination of clinical variables explained here, we have a powerful tool at our fingertips to try and combat cardiovascular illness before it even has the chance to take root!
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Additional Information
| Field | Value |
|---|---|
| Data last updated | October 8, 2024 |
| Metadata last updated | October 8, 2024 |
| Created | October 8, 2024 |
| Format | ZIP |
| License | No License Provided |
| Datastore active | False |
| Has views | False |
| Id | 951586e6-a6ba-47fe-a037-44024b932bf2 |
| Mimetype | application/zip |
| Package id | bea31fef-e58e-4b97-a1b8-7c2eaca5b7b3 |
| Position | 0 |
| Size | 3.9 KiB |
| State | active |
| Url type | upload |